March 22, 2023
image is taken from https://link.springer.com/article/10.1007/s00418-011-0903-2
link to the review https://www.nature.com/articles/s41592-022-01409-2 and link to the Museum of Transcriptomics
link to the review https://www.nature.com/articles/s41592-022-01409-2 and link to the Museum of Transcriptomics
link to the review https://www.nature.com/articles/s41592-022-01409-2 and link to the Museum of Transcriptomics
Current-era technologies
link to the review https://www.nature.com/articles/s41592-022-01409-2 and link to the Museum of Transcriptomics
Current-era technologies
Due to the similarity in the nature of the data with scRNA-seq:
In cases where the spots are too small you will need segmentation algorithms instead
Figure 1 from https://www.nature.com/articles/s42003-020-01247-y
Figure 1 from https://www.nature.com/articles/nmeth.4636
Taken from https://immunox.ucsf.edu/sites/immunox.ucsf.edu/files/pdf/Micro204_Anat_IR%20v2018.pdf, but Abbas listed as sources so this image could be from the textbook “Cellular and Molecular Immunology”
We will try to identify
Taken from https://immunox.ucsf.edu/sites/immunox.ucsf.edu/files/pdf/Micro204_Anat_IR%20v2018.pdf, but Abbas listed as sources so this image could be from the textbook “Cellular and Molecular Immunology”
lymph_node <- Load10X_Spatial(data.dir = ".", filename = "spatial/V1_Human_Lymph_Node_filtered_feature_bc_matrix.h5")
lymph_node
## An object of class Seurat
## 36601 features across 4035 samples within 1 assay
## Active assay: Spatial (36601 features, 0 variable features)
## 1 image present: slice1de_markers <- FindAllMarkers(lymph_node, only.pos = T)
head(de_markers)
## p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## CCL21 9.472831e-114 0.4699865 1.000 1.000 2.014019e-109 0 CCL21
## CCL2 1.570773e-84 0.5363093 0.999 0.961 3.339620e-80 0 CCL2
## C7 3.203259e-49 0.3513696 0.993 0.952 6.810449e-45 0 C7
## JCHAIN 2.822487e-47 0.2504207 1.000 1.000 6.000889e-43 0 JCHAIN
## CEBPD 4.478532e-47 0.3927098 0.965 0.875 9.521807e-43 0 CEBPD
## IGHA1 6.160128e-45 0.3404398 1.000 1.000 1.309705e-40 0 IGHA1goodMarkers <- de_markers %>% filter(cluster == 0) %>% slice_min(order_by = p_val_adj, n = 4) %>% pull(gene)
SpatialDimPlot(lymph_node, cells.highlight = CellsByIdentities(object = lymph_node, idents = c(0)), facet.highlight = TRUE)
SpatialFeaturePlot(object = lymph_node, features = goodMarkers, alpha = c(0.1, 1), ncol = 2)goodMarkers <- de_markers %>% filter(cluster == 1) %>% slice_min(order_by = p_val_adj, n = 4) %>% pull(gene)
SpatialDimPlot(lymph_node, cells.highlight = CellsByIdentities(object = lymph_node, idents = c(1)), facet.highlight = TRUE)
SpatialFeaturePlot(object = lymph_node, features = goodMarkers, alpha = c(0.1, 1), ncol = 2)Taken from https://www.sciencedirect.com/science/article/pii/S1438422121000217
goodMarkers <- de_markers %>% filter(cluster == 2) %>% slice_min(order_by = p_val_adj, n = 4) %>% pull(gene)
SpatialDimPlot(lymph_node, cells.highlight = CellsByIdentities(object = lymph_node, idents = c(2)), facet.highlight = TRUE)
SpatialFeaturePlot(object = lymph_node, features = goodMarkers, alpha = c(0.1, 1), ncol = 2)goodMarkers <- de_markers %>% filter(cluster == 3) %>% slice_min(order_by = p_val_adj, n = 4) %>% pull(gene)
SpatialDimPlot(lymph_node, cells.highlight = CellsByIdentities(object = lymph_node, idents = c(3)), facet.highlight = TRUE)
SpatialFeaturePlot(object = lymph_node, features = goodMarkers, alpha = c(0.1, 1), ncol = 2)goodMarkers <- de_markers %>% filter(cluster == 4) %>% slice_min(order_by = p_val_adj, n = 4) %>% pull(gene)
SpatialDimPlot(lymph_node, cells.highlight = CellsByIdentities(object = lymph_node, idents = c(4)), facet.highlight = TRUE)
SpatialFeaturePlot(object = lymph_node, features = goodMarkers, alpha = c(0.1, 1), ncol = 2)goodMarkers <- de_markers %>% filter(cluster == 5) %>% slice_min(order_by = p_val_adj, n = 4) %>% pull(gene)
SpatialDimPlot(lymph_node, cells.highlight = CellsByIdentities(object = lymph_node, idents = c(5)), facet.highlight = TRUE)
SpatialFeaturePlot(object = lymph_node, features = goodMarkers, alpha = c(0.1, 1), ncol = 2)goodMarkers <- de_markers %>% filter(cluster == 6) %>% slice_min(order_by = p_val_adj, n = 4) %>% pull(gene)
SpatialDimPlot(lymph_node, cells.highlight = CellsByIdentities(object = lymph_node, idents = c(6)), facet.highlight = TRUE)
SpatialFeaturePlot(object = lymph_node, features = goodMarkers, alpha = c(0.1, 1), ncol = 2)goodMarkers <- de_markers %>% filter(cluster == 7) %>% slice_min(order_by = p_val_adj, n = 4) %>% pull(gene)
SpatialDimPlot(lymph_node, cells.highlight = CellsByIdentities(object = lymph_node, idents = c(7)), facet.highlight = TRUE)
SpatialFeaturePlot(object = lymph_node, features = goodMarkers, alpha = c(0.1, 1), ncol = 2)goodMarkers <- de_markers %>% filter(cluster == 8) %>% slice_min(order_by = p_val_adj, n = 4) %>% pull(gene)
SpatialDimPlot(lymph_node, cells.highlight = CellsByIdentities(object = lymph_node, idents = c(8)), facet.highlight = TRUE)
SpatialFeaturePlot(object = lymph_node, features = goodMarkers, alpha = c(0.1, 1), ncol = 2)Moran’s I, https://en.wikipedia.org/wiki/Moran%27s_I
Please install Rfast2 if you want to run it
Systems Biology - Gene Expression - 2023